Sustainability themed problem solving in data structures and algorithms

Erkan, Ali; Pfaff, Tom; Hamilton, Jason G.; Rogers, Michael

doi:10.1145/2157136.2157146

Cited by 11 publications

(10 citation statements)

References 19 publications

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“…The ITiCSE working group [12] offers 14 projects related to "social good" that motivate students and provide them with skills to solve complex problems; Erkan et al [11] do the same for a data structures course. By contrast, we weave the theme of solving problems with real-world impact throughout the course.…”

Section: Related Workmentioning

confidence: 99%

A Data Programming CS1 Course

Anderson

Ernst

Ordóñez

et al. 2015

Proceedings of the 46th ACM Technical Symposium on Computer Science Education

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This paper reports on our experience teaching introductory programming by means of real-world data analysis. We have found that students can be motivated to learn programming and computer science concepts in order to analyze DNA, predict the outcome of elections, detect fraudulent data, suggest friends in a social network, determine the authorship of documents, and more. The approach is more than just a collection of "nifty assignments"; rather, it affects the choice of topics and pedagogy. This paper describes how our approach has been used at four diverse colleges and universities to teach CS majors and nonmajors alike. It outlines the types of assignments, which are based on problems from science, engineering, business, and the humanities. Finally, it offers advice for anyone trying to integrate the approach into their own institution.

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Section: Related Workmentioning

confidence: 99%

A Data Programming CS1 Course

Anderson

Ernst

Ordóñez

et al. 2015

Proceedings of the 46th ACM Technical Symposium on Computer Science Education

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“…The strategy of using a single problem domain as a thread throughout an entire course has also been advocated by Erkan et al [5] and investigated by Lobo and Baliga [11,12]. Lobo and Baliga have developed sequences of programming projects for three different problem domains: the Travelling Salesman Problem, Boolean Formula Satisfiability, and Sudoku.…”

Section: Student Learning Outcomes and Related Workmentioning

confidence: 99%

“…For example, Figure 1 shows a graph with n = 8 and m = 9. The edges (0,1), (2,5), and (4,3) form a matching in this graph.…”

Section: Problem Domainmentioning

confidence: 99%

Illustrating the Interaction of Algorithms and Data Structures Using the Matching Problem

Lucas

2015

Proceedings of the 46th ACM Technical Symposium on Computer Science Education

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A thorough understanding of algorithms and data structures is essential for any student of Computer Science. In this paper we present a sequence of programming projects that elegantly integrate both the study of algorithmic paradigms with an illustration of how the choice of data structure significantly impacts an algorithm. We do this in the context of a single, easily understood problem domain, that of finding a maximum matching in a graph. By focusing on only a single problem, students more fully appreciate the concept that any problem can be solved in a variety of different ways.

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“…The ITiCSE working group [4] offers 14 projects related to "social good" that motivate students and provide them with skills to solve complex problems; Erkan et al [3] do the same for a data structures class. We weave the themes throughout the class.…”

Section: Comparison To Other Teaching Approachesmentioning

confidence: 99%

Introductory programming meets the real world

Anderson

Ernst

Ordóñez

et al. 2014

Proceedings of the 45th ACM Technical Symposium on Computer Science Education

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Too many students in introductory programming classes fail to understand the significance and utility of the concepts being taught. Their low motivation impacts their learning. One contributing factor is pedagogy that emphasizes computing for its own sake and assignments that are abstract, such as computing the factorial function.Many educators have improved on such traditional approaches by teaching concepts in contexts that students find more relevant, such as games, robots, and media. Now, it is time to take the next step.In this special session, participants will develop and discuss ways to teach introductory programming by means of real-world data analysis problems from science, engineering, business, and the humanities. Students can be motivated to learn programming in order to analyze DNA, predict the outcome of elections, detect fraudulent data, suggest friends in a social network, determine the authorship of texts, and more (see Section 3.4 for more examples). The approach is more than just a collection of "nifty assignments": rather, it affects the choice of topics and pedagogy, all of which together lead to greater student satisfaction.The approach has been successfully used at 4 colleges and universities. The classes were effective for both CS and non-CS majors. Neither the computing material nor the problems need to be "dumbed down". At the end of the term students were amazed and delighted at the real data analysis that they could perform. They were excited about applying computation in their work and about learning more.The special session contains a mix of activities, including comparative analysis of introductory classes; group discussion of curriculum design; a mini-panel discussing how the approach has worked in practice; and brainstorming about example assignments and curriculum revision.

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Sustainability themed problem solving in data structures and algorithms

Cited by 11 publications

References 19 publications

A Data Programming CS1 Course

A Data Programming CS1 Course

Illustrating the Interaction of Algorithms and Data Structures Using the Matching Problem

Introductory programming meets the real world

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